Daniel Ward

Daniel Ward

Computer Science - Robotics - Learning

Scalable learning for bridging the species gap in image-based plant phenotyping

Published in CVIU (under review), 2020

Recommended citation: Ward, D., & Moghadam, P. (2020). Scalable learning for bridging the species gap in image-based plant phenotyping. arXiv preprint arXiv:2003.10757. https://arxiv.org/abs/2003.10757

Paper

Project Webpage The paper can be found here.

Code

The code can be obtained here.

Data and Pretrained Model

The synthetic data generated for this study can be downloaded here

Attribution

If you use the data, code or pretrained models please cite:

Ward, D., & Moghadam, P. (2020). Scalable learning for bridging the species gap in image-based plant phenotyping. arXiv preprint arXiv:2003.10757.

Bibtext

@article{ward2020scalable,
  title={Scalable learning for bridging the species gap in image-based plant phenotyping},
  author={Ward, Daniel and Moghadam, Peyman},
  journal={arXiv preprint arXiv:2003.10757},
  year={2020}
}